Comparing Local Descriptors and Bags of Visual Words to Deep Convolutional Neural Networks for Plant Recognition

The use of machine learning and computer vision methods for recognizing different plants from images has attracted lots of attention from the community. This paper aims at comparing local feature descriptors and bags of visual words with different classifiers to deep convolutional neural networks (CNNs) on three plant datasets; AgrilPlant, LeafSnap, and Folio. To achieve this, we study the use of both scratch and fine-tuned versions of the GoogleNet and the AlexNet architectures and compare them to a local feature descriptor with k-nearest neighbors and the bag of visual words with the histogram of oriented gradients combined with either support vector machines and multi-layer perceptrons. The results shows that the deep CNN methods outperform the hand-crafted features. The CNN techniques can also learn well on a relatively small dataset, Folio

Multi-script handwritten character recognition: Using feature descriptors and machine learning

Handwritten character recognition plays an important role in transforming raw visual image data obtained from handwritten documents using for example scanners to a format which is understandable by a computer. It is an important application in the field of pattern recognition, machine learning and artificial intelligence. There are already different handwritten character recognition systems that have been designed for commercial purposes, such as mail sorting and bank cheque processing. Furthermore, this type of research can help to search through different historical handwritten manuscript collections. In this way the cumulative historical information can become accessible to a wide public. In this PhD research, several methods are proposed to deal with several challenges that occur when trying to recognize handwritten characters from multiple language scripts. The thesis contributes to all levels of processing isolated character images: from intensity normalization to segmentation, and from feature extraction to the final classification. Moreover, solutions are proposed for recognizing isolated handwritten character images when not very many handwritten character examples are available. The main goal of the research presented in this dissertation is to study robust feature extraction techniques and machine learning techniques for handwritten character recognition. The best techniques are the combination of the histogram of oriented gradients with bags of visual words. Furthermore, a new method for line segmentation is proposed, which is a part of document layout analysis. The novel techniques have been tested on many different scripts and the results show that they effectively address the problems of line segmentation and character recognition

Effective Data Augmentation and Training Techniques for Improving Deep Learning in Plant Leaf Disease Recognition

Plant disease is the most common problem in agriculture. Usually, the symptoms appear on leaves of the plants which allow farmers to diagnose and prevent the disease from spreading to other areas. An accurate and consistent plant disease recognition system can help prevent the spread of diseases and save maintenance costs. In this research, we present a plant leaf disease recognition system using two deep convolutional neural networks (CNNs); MobileNetV2 and NasNetMobile. These CNN architectures are designed to be suitable for smartphones due to the models being small. We have experimented on training techniques; online, offline, and mixed training techniques on two plant leaf diseases. As for data augmentation techniques, we found that the combination of rotation, shift, and zoom techniques significantly increases the performance of the CNN architectures. The experimental results show that the most accurate algorithm for plant leaf disease recognition is NASNetMobile architecture using transfer learning. Additionally, the most accurate result is obtained when combining the offline training technique with data augmentation techniques

Handwritten Character Classification using the Hotspot Feature Extraction Technique

Feature extraction techniques can be important in character recognition, because they can enhance the efficacy of recognition in comparison to featureless or pixel-based approaches. This study aims to investigate the novel feature extraction technique called the hotspot technique in order to use it for representing handwritten characters and digits. In the hotspot technique, the distance values between the closest black pixels and the hotspots in each direction are used as representation for a character. The hotspot technique is applied to three data sets including Thai handwritten characters (65 classes), Bangla numeric (10 classes), and MNIST (10 classes). The hotspot technique consists of two parameters including the number of hotspots and the number of chain code directions. The data sets are then classified by the k-Nearest Neighbors algorithm using the Euclidean distance as function for computing distances between data points. In this study, the classification rates obtained from the hotspot, mark direction, and direction of chain code techniques are compared. The results revealed that the hotspot technique provides the largest average classification rates